BACKGROUND: There are limited resources to detect and interpret cluster signals in resource-limited hospitals. Our aim is to improve the interpretation of pathogen spatiotemporal clustering detected using the SaTScan algorithm- a method that uses space-time scan statistics to detect cluster signals that occurred more often than expected. METHODS: We analysed electronic data of inpatients with clinical specimen culture positive for seven antimicrobial-resistant pathogens in two tertiary hospitals in Thailand from January to December 2022. We applied space-time uniform scan statistics in SaTScan. We performed four analyses. Analysis 1 did not include antimicrobial susceptibility test (AST) result profiles. Analyses 2, 3, and 4 included AST results of antibiotics that had ≥70%, ≥80%, and ≥90% of available results among the included patients, respectively. FINDINGS: There were 125,848 microbiology data records collected from a 1,188-bed hospital and 54,069 records from a 773-bed hospital in 2022. Multiple cluster signals were detected in both hospitals; including clusters of carbapenem-resistant Gram-negative organisms across different wards over different time periods. The number of cluster signals detected decreased with increasing thresholds used to select antibiotics to be included in the analysis. For instance, Analysis 2 detected 33 clusters, which reduced to 4 clusters in Analysis 4 in the 1,188-bed hospital data. Similar patterns were also observed in the 773-bed hospital data. The temporal occurrence of detected cluster signals coincided with the period during which AST results were unavailable in Analysis 2 and 3. INTERPRETATION: Our findings suggest that SaTScan is applicable to detect potential cluster signals in resource-limited settings, and the interpretation of detected signals could be supported by graphical presentations of temporal changes in the availability of AST data.